Optimization

Optimization is the driving force behind a model’s learning process. During training, the system continuously updates its parameters in order to reduce the loss function or improve a target objective. The healthier this process runs, the better the model learns. But optimization is not only a technical computation problem; it also affects learning speed, stability, and the quality of the final solution. A poorly optimized model can turn a good idea into a weak-performing system. That is why optimization is a much more central topic in AI engineering than it may first appear.